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Monte Carlo simulation for moment-independent sensitivity analysis

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  • Wei, Pengfei
  • Lu, Zhenzhou
  • Yuan, Xiukai

Abstract

The moment-independent sensitivity analysis (SA) is one of the most popular SA techniques. It aims at measuring the contribution of input variable(s) to the probability density function (PDF) of model output. However, compared with the variance-based one, robust and efficient methods are less available for computing the moment-independent SA indices (also called delta indices). In this paper, the Monte Carlo simulation (MCS) methods for moment-independent SA are investigated. A double-loop MCS method, which has the advantages of high accuracy and easy programming, is firstly developed. Then, to reduce the computational cost, a single-loop MCS method is proposed. The later method has several advantages. First, only a set of samples is needed for computing all the indices, thus it can overcome the problem of “curse of dimensionality†. Second, it is suitable for problems with dependent inputs. Third, it is purely based on model output evaluation and density estimation, thus can be used for model with high order (>2) interactions. At last, several numerical examples are introduced to demonstrate the advantages of the proposed methods.

Suggested Citation

  • Wei, Pengfei & Lu, Zhenzhou & Yuan, Xiukai, 2013. "Monte Carlo simulation for moment-independent sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 110(C), pages 60-67.
    • Handle: RePEc:eee:reensy:v:110:y:2013:i:c:p:60-67
    DOI: 10.1016/j.ress.2012.09.005
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    1. Marco Ratto & Andrea Pagano, 2010. "Using recursive algorithms for the efficient identification of smoothing spline ANOVA models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 94(4), pages 367-388, December.
    2. Sobol’, I.M. & Kucherenko, S., 2009. "Derivative based global sensitivity measures and their link with global sensitivity indices," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(10), pages 3009-3017.
    3. Borgonovo, E., 2007. "A new uncertainty importance measure," Reliability Engineering and System Safety, Elsevier, vol. 92(6), pages 771-784.
    4. Ratto, M. & Pagano, A. & Young, P.C., 2009. "Non-parametric estimation of conditional moments for sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 237-243.
    5. Tarantola, S. & Gatelli, D. & Mara, T.A., 2006. "Random balance designs for the estimation of first order global sensitivity indices," Reliability Engineering and System Safety, Elsevier, vol. 91(6), pages 717-727.
    6. Liu, Qiao & Homma, Toshimitsu, 2009. "A new computational method of a moment-independent uncertainty importance measure," Reliability Engineering and System Safety, Elsevier, vol. 94(7), pages 1205-1211.
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    Cited by:

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    8. Xie, Xiangzhong & Schenkendorf, René & Krewer, Ulrike, 2019. "Efficient sensitivity analysis and interpretation of parameter correlations in chemical engineering," Reliability Engineering and System Safety, Elsevier, vol. 187(C), pages 159-173.
    9. Derennes, Pierre & Morio, Jérôme & Simatos, Florian, 2019. "A nonparametric importance sampling estimator for moment independent importance measures," Reliability Engineering and System Safety, Elsevier, vol. 187(C), pages 3-16.
    10. Yun, Wanying & Lu, Zhenzhou & Jiang, Xian, 2019. "An efficient method for moment-independent global sensitivity analysis by dimensional reduction technique and principle of maximum entropy," Reliability Engineering and System Safety, Elsevier, vol. 187(C), pages 174-182.
    11. Yun, Wanying & Lu, Zhenzhou & Feng, Kaixuan & Li, Luyi, 2019. "An elaborate algorithm for analyzing the Borgonovo moment-independent sensitivity by replacing the probability density function estimation with the probability estimation," Reliability Engineering and System Safety, Elsevier, vol. 189(C), pages 99-108.
    12. Yishang Zhang & Yongshou Liu & Xufeng Yang & Bin Zhao, 2015. "An efficient Kriging method for global sensitivity of structural reliability analysis with non-probabilistic convex model," Journal of Risk and Reliability, , vol. 229(5), pages 442-455, October.
    13. Tong, Ming-Na & Zhao, Yan-Gang & Lu, Zhao-Hui, 2021. "Normal transformation for correlated random variables based on L-moments and its application in reliability engineering," Reliability Engineering and System Safety, Elsevier, vol. 207(C).

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